As online education has become more widespread, developing effective practices for providing effective and timely feedback to both instructors and students on student performance and learning has become increasingly more important. However, many of the currently available tools and techniques for providing feedback on student progress are relatively primitive.
For example, in a traditional learning environment, instructors may grade written homework assignments submitted by students and then return the corrected assignments after some delay, perhaps a week later. In learning environments that employ interactive computer-based learning activities, the feedback cycle is typically shorter. Such computer-based systems may be programmed to correct errors in student responses as they are made. For example, when a student answers the question “83−47=?” by typing in “46”, the system can recognize and communicate in real-time to the student that the answer of “46” is incorrect. Even though this kind of computer-based feedback is timely and saves instructor effort, it has several serious limitations. First, it deals with only one response at a time, and thus cannot detect patterns in student learning across questions or across time. Second, it processes only one student at a time and so cannot detect similarities across a set of students taking the same course. Third, the instructor is not privy to the full interaction of the student with the teaching system and thus has no opportunity to understand or respond to how students are performing.
In view of the foregoing issues, enhanced tools, techniques and strategies are needed for analyzing student learning and performance in a learning environment, including online and computer-based environments.